Add AI Safety section to documentation

Added a section on AI Safety and Trustworthiness, detailing key areas of study including bias, robustness, security, and best practices for generative AI.

Author: vishwaparekh

research/ai-safety.md

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+---
+layout: default
+title: AI Safety
+---
+
+## AI Safety & Trustworthiness
+
+We study when and how AI systems fail, and how to make their behavior more reliable in critical settings.
+
+This includes:
+
+- **Bias and fairness** – detecting and characterizing underdiagnosis and demographic leakage in imaging AI and language models.
+- **Robustness to clinical variation** – stress-testing models under real-world shifts in acquisition, scanners, and protocols.
+- **Security & adversarial bias** – understanding “hidden in plain sight” attacks and other subtle ways systems can be manipulated.
+- **Best practices for generative AI** – guidelines for the safe use of large language models in radiology and clinical workflows.
+
+The goal is to design evaluation frameworks and mitigation strategies that go beyond accuracy, placing safety and trust at the center of AI deployment.